GPU Accelerated K Means Clustering Refined using ANT Colony Optimization

Abstract

Discovery of clusters using parallelization is the answer for handling computational issues invoked by large datasets in cluster analysis. The Graphics Processing Unit and Compute Unified Device Architecture form a joint platform to improvise parallel clustering algorithms. The parallel K-Means clustering algorithm improves the speedup of clustering but often faces with problems of falling into local optima. The meta-heuristic algorithm used to optimize the clustering process using pheromone updation is known as Ant Colony Optimization technique. The K-Means clustering solution is fine tuned by applying parallel implementation of Ant Colony Optimization K-Means Clustering in order to increase efficiency. The aim is to combine optimization characteristic of Ant Colony Optimization for calculation of centroids with clustering attitude of K-Means and enhance the solution using processing power of GPU. The parallelization strategy utilized exhibits variations in speedup and quality of the clusters. The Parallel Ant Colony Clustering algorithm is tested on standard datasets and performance is compared with Sequential K-Means, Parallel K-Means, Sequential Tabu K-Means, Parallel Tabu K-Means and Sequential Ant Colony Clustering algorithms. The experimental results confirm yet another parallelization technique to unravel data clustering problems.